Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: The Temporal Query Model (TQNet) for MetaTrader 5
The TQNet framework opens up new possibilities for modeling and forecasting financial time series by combining modularity, flexibility, and high performance. The article explores the possibility of implementing complex mechanisms for handling global correlations, including advanced parameter
Neural Networks in Trading: Decomposition Instead of Scaling (Conclusion) for MetaTrader 5
We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data
Neural Networks in Trading: Decomposition Instead of Scaling — Building Modules for MetaTrader 5
In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in
Neural Networks in Trading: Decomposition Instead of Scaling (SSCNN) for MetaTrader 5
In this article, we begin our exploration of the SSCNN framework — a modern architectural solution for time series analysis that combines accuracy, a structured design, and high computational efficiency. We will systematically examine its theoretical aspects, highlight the key differences from its
Neural Networks in Trading: Disentangling Structured Components (Conclusion) for MetaTrader 5
The article provides a detailed explanation of the SCNN architecture and one way to implement it using MQL5. We will show how time series decomposition can be combined with neural network methods and attention mechanisms
Neural Networks in Trading: Disentangling Structured Components (Encoder) for MetaTrader 5
We invite you to explore the next stage in implementing the SCNN framework, which combines flexibility and interpretability, allowing structural components of a time series to be identified precisely. The article provides a detailed explanation of the mechanisms of adaptive normalization and
Neural Networks in Trading: Disentangling Structured Components (SCNN) for MetaTrader 5
We invite you to explore the innovative SCNN framework, which takes time series analysis to a new level by clearly separating data into long-term, seasonal, short-term, and residual components. This approach significantly improves forecasting accuracy by allowing the model to adapt to complex and
Neural Networks in Trading: An End-to-End Multivariate Time Series Forecasting Model (Conclusion) for MetaTrader 5
We are pleased to present the final part of our series on GinAR — a neural network framework for time series forecasting. In this article, we analyze the results of testing the model on new data and assess its robustness under real-market conditions
Neural Networks in Trading: An End-to-End Multivariate Time Series Forecasting Model (Key Components) for MetaTrader 5
We invite you to explore a new implementation of the key components of the GinAR framework — an adaptive algorithm for working with graph-structured time series. This article provides a step-by-step breakdown of the architecture and the algorithms for the forward pass and error backpropagation
Neural Networks in Trading: An End-to-End Multivariate Time Series Forecasting Model (GinAR) for MetaTrader 5
We invite you to explore an innovative approach to forecasting time series with missing data using the GinAR framework. The article demonstrates the implementation of key components using OpenCL, which ensures high performance. In our next publication, we will take a detailed look at how to